Course Description:
Fundamental theory and algorithms of optimization, emphasizing convex optimization, with applications to a wide range of fields. The geometry of convex sets, basic convex analysis, optimality conditions, duality. Numerical algorithms: steepest descent, Newton's method, interior point methods. Applications from statistics, communications, control, signal processing, physics, and economics.
Prerequisites: linear algebra and differential calculus.
Course Website:
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Last modified on January 15, 2006